Predicting outcome of daycare cognitive behavioural therapy in a naturalistic sample of patients with PTSD: a machine learning approach.

Background: Identifying predictors for treatment outcome in patients with posttraumatic stress disorder (PTSD) is important in order to provide an effective treatment, but robust and replicated treatment outcome predictors are not available up to now. Objectives: We investigated predictors of treatment outcome in a naturalistic sample of patients with PTSD admitted to an 8-week daycare cognitive behavioural therapy programme following a wide range of traumatic events. Method: We used machine learning (linear and non-linear regressors and cross-validation) to predict outcome at discharge for 116 patients and sustained treatment effects 6 months after discharge for 52 patients who had a follow-up assessment. Predictions were based on a wide selection of demographic and clinical assessments including age, gender, comorbid psychiatric disorders, trauma history, posttraumatic symptoms, posttraumatic cognitions, depressive symptoms, general psychopathology and psychosocial functioning. Results: We found that demographic and clinical variables significantly, but only modestly predicted PTSD treatment outcome at discharge (r = 0.21, Conclusion: Our results underscore the importance of early treatment response for short- and long-term treatment success. Nevertheless, it remains an unresolved challenge to identify variables that can robustly predict outcome before the initiation of treatment.